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--- |
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license: mit |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: val |
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path: data/val-* |
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- split: val_dense |
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path: data/val_dense-* |
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- split: val_sparse |
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path: data/val_sparse-* |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 825600000 |
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num_examples: 1600000 |
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- name: val |
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num_bytes: 8256000 |
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num_examples: 16000 |
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- name: val_dense |
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num_bytes: 2064000 |
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num_examples: 4000 |
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- name: val_sparse |
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num_bytes: 82560000 |
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num_examples: 160000 |
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download_size: 354675733 |
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dataset_size: 918480000 |
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--- |
|
|
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Data for [**Flip-Flop Language Modeling**](https://arxiv.org/abs/2306.00946). The task is to correctly execute the sequential operations of a 1-bit register. The Transformer architecture, despite being apparently built for this operation, makes sporadic extrapolation errors (*attention glitches*). An open challenge is to fix these without recourse to long-tailed data or a recurrent architecture. Splits reflect the FFLM setup from the paper: |
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- `train`: 1.6M sequences from FFL(0.8) *(256 instructions, 80% ignore, 10% read, 10% write)*. |
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- `val`: 16K sequences from FFL(0.8). |
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- `val_dense`: 4K sequences from FFL(0.1). |
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- `val_sparse`: 160K sequences from FFL(0.98). |
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|
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Usage |
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--- |
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```python |
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import torch |
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import datasets |
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|
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dataset = datasets.load_dataset('synthseq/flipflop') |
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dataset['train'][0] # {'text': 'w1i1w0i0 ... |
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|
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def tokenize_batch(batch): |
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mapping = {'w': 0, 'r': 1, 'i': 2, '0': 3, '1': 4} |
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tokenized_batch = [[mapping[char] for char in s] for s in batch['text']] |
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return {'tokens': torch.tensor(tokenized_batch, dtype=torch.int64)} |
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|
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dataset.set_transform(tokenize_batch) |
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dataset['train'][0] # {'tokens': tensor([0, 4, 2, 4, 0, 3, 2, 3, 2 ... |
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``` |
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|
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Citation |
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--- |
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|
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``` |
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@article{liu2023exposing, |
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title={Exposing Attention Glitches with Flip-Flop Language Modeling}, |
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author={Liu, Bingbin and Ash, Jordan T and Goel, Surbhi and Krishnamurthy, Akshay and Zhang, Cyril}, |
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journal={arXiv preprint arXiv:2306.00946}, |
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year={2023} |
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} |
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``` |
